LIDAR and Infrared Cameras for ADAS and Autonomous Sensing C1896
Topics: Advanced Technologies
This course examines ADAS and autonomous vehicle technologies that offer the potential to increase safety while attempting to optimize the cost of car ownership. LIDAR (light detection ranging) and Infrared camera sensing are seeing a rapid growth and adoption in the industry. However, the sensor requirements and system architecture options continue to evolve almost every six months. This course will provide the foundation to build on for these two technologies in automotive applications. It will include a demonstration model for LIDAR and Infrared camera.
>The course will begin with a review of Infrared basics - electromagnetic spectrum, spectral irradiance, night vision and eye safety. We will then dive into LIDAR – flash, scanning, wavelengths, lasers, detectors, scanners, range and resolution calculations, optics, thermal design, challenges to automotive qualification and sensor fusion. The second half will cover Infrared camera topics with focus on driver monitoring for interior and machine vision for exterior. We will review rolling and global shutter imagers, wavelength selection, use of secondary optics, continuous vs pulsed IRED operation, thermal design, power consumption, eye safety certification and HMI considerations. A brief review of iris recognition, cabin monitoring and face recognition. We will end with a short discussion on trends and challenges facing optical sensing in autonomous vehicles.
Learning Objectives
Upon completion of this course the participants will be able to:
- Comprehend electromagnetic spectrum, spectral irradiance, night vision and eye safety
- Describe various LIDAR architectures based on key design parameters
- Formulate LIDAR requirements based on an understanding of system edge use cases
- Prescribe Infrared camera requirements with a comprehension of key variables
- Calculate IRED and Laser power requirements to meet system needs
- Gain an overview of challenges and opportunities for sensing trends in ADAS and AV
Who Should Attend
Mechanical, lead, application, electronical engineers, head of innovation and BOM family owners, professionals involved in active safety, LIDAR, driver monitoring, machine vision, and automated driving
Prerequisites
An undergraduate engineering degree or a strong technical background is highly recommended. A basic knowledge of college algebra, college physics, and a basic awareness of LIDAR and Infrared camera applications in ADAS and autonomous vehicles will be beneficial.
You must complete all course contact hours and successfully pass the learning assessment to obtain CEUs.
Rajeev Thakur
Rajeev Thakur is currently Director Automotive Programs at Velodyne Lidar - responsible for building Velodyne's LIDAR business in the automotive market. In this role he supports OEM customers to select, design-in and launch Velodyne's wide LIDAR portfolio for autonomous vehicles and ADAS functions. Prior to this, he was at OSRAM Opto Semiconductors as Regional Marketing Manager for infrared product management and business development in the NAFTA automotive market. His focus was on LIDAR, driver monitoring, night vision, blind spot detection and other ADAS applications. He has been in the Detroit automotive industry since 1990 – working for companies such as Bosch, Johnson Controls and Chrysler. He has concept-to-launch experience in occupant sensing, seating and power train sensors. He holds a masters degree in Manufacturing engineering from the University of Massachusetts, Amherst and a Bachelors degree in Mechanical engineering from Guindy engineering college in Chennai, India. He is a licensed professional engineer and holds a number of patents on occupant sensing. He is also a member of the SAE Active Safety Standards development committee and a reviewer for IEEE Intelligent Transportation Systems Transactions.
